Abstract
Two three-dimensional localization algorithms for a swarm of underwater vehicles are presented. The first is grounded on an extended Kalman filter (EKF) scheme used to fuse some proprioceptive data such as the vessel's speed and some exteroceptive measurements such as the time of flight (TOF) sonar distance of the companion vessels. The second is a Monte Carlo particle filter localization processing the same sensory data suite. The results of several simulations using the two approaches are presented, with comparison. The case of a supporting surface vessel is also considered. An analysis of the robustness of the two approaches against some system parameters is given.
Highlights
The exploration of the oceans, both for scientific and economic purposes, is becoming increasingly important
One of its more promising branches is that of autonomous underwater vehicles (AUV), i.e., those vehicles that are capable of performing the required tasks without human supervision, coping with missions unknown
The main drawback of a Kalman approach in underwater swarm localization is represented by the large quantity of data that must be circulated among the vessels: all the relevant matrices of the algorithm, which, in addition, grow non-linearly with the number of considered robots
Summary
The exploration of the oceans, both for scientific and economic purposes, is becoming increasingly important. In this work two different approaches for robot localization in a swarm of underwater vehicles are compared: one based on Kalman filtering and the second on a Monte Carlo scheme. The main drawback of a Kalman approach in underwater swarm localization is represented by the large quantity of data that must be circulated among the vessels: all the relevant matrices of the algorithm, which, in addition, grow non-linearly with the number of considered robots. A further aspect of an ultrasound-based localization system is related to the speed of the communication in the water In the last section conclusions and future work are discussed
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